Finite time adaptive neural intermittent control for a class of nonlinear dynamical systems

Abstract

In this paper, the semiglobal practical finite-time stable (SGPFTS) problem for a class of nonlinear nonstrict feedback systems is investigated by adaptive neural networks (NNs) intermittent control strategy. This work is motivated by that available differential inequalities to cope with intermittent control do not work for nonstrict feedback nonlinear system (NFNS), since the system dynamics is unknown. In order to deal with this problem, a novel differential inequality and adaptive neural intermittent control are established to handle intermittent control, which generalizes previous works. Then, based on this lemma with the proposed adaptive NNs intermittent control scheme, backstepping design process, finite-time technique and Lyapunov stability theory, sufficient conditions are presented to guarantee the achievement of SGPFTS for NFNS. Furthermore, a networked-based one-link arm dynamics system is applied to the theoretical results. Finally, corresponding simulation results are given to illustrate the effectiveness of the theoretical results and the practicability of the proposed control method.

Governance and Efficiency in Brazilian Water Utilities: An Analysis from Revenue Collection Efficiency

“…To address this, [53] introduced production frontiers with dynamic DEA. Following this perspective, [54,55], [56,57,58], [59], [60], [61], and [62] expanded the scope of the DEA additive model known as the Slack Based Model (SBM) to a dynamic model capable of estimating the production frontier over several periods: the Dynamic Slack Based Model (DSBM).…”